In-Home Monitoring Sleep Turnover Activities and Breath Rate via WiFi Signals

In-home sleep monitoring is essential for evaluating sleep quality of individuals. Although many sleep monitoring systems have been developed recently, they have limitations in achieving a good performance at low cost. To address this issue, this article proposes a new system based on channel-state information of domestic WiFi network to monitor both turnover activities and breathing rate of sleepers. Unlike recent approaches placing receiving antennas close to each other, scattered placement is adopted to fully exploit spatial diversity of receiving antennas. More importantly, a new error correction method is proposed to accurately recognize turnover activities. Based on the interrelation between consecutive activities, the proposed method can effectively correct the recognition errors of existing methods including convolutional neural network. Then, for accurately estimating breathing rate, both a new subcarrier selection method and a new peak identification method are proposed. Experiment results show that our system can significantly improve the recognition accuracy of eight typical sleep turnover activities and four typical sleep postures. We can achieve the mean accuracy of 94.59% and 95.83% on the recognition of turnover activities and sleep postures, respectively. Besides, our system can also significantly improve the estimation accuracy of breathing rate especially in tough scenarios, such as prone and side-lying positions.